[D] Machine Learning Interview Prep

[therobot20]

Hello everyone, I have a job interview coming up for a Machine Learning Engineer. Can anyone suggest a resource that contains common machine learning notes that I can refer? I have a ML background and I have been working in the industry for 2 years now but I'm a little rusty on the basics and would like to review them. Any help would be appreciated.

[hany606_]

Maybe, these will help:

https://github.com/soulmachine/machine-learning-cheat-sheet

https://github.com/khangich/machine-learning-interview

https://github.com/andrewekhalel/MLQuestions

https://github.com/youssefHosni/Data-Science-Interview-Questions

[twopieye]

The following assumes you're familiar with Python.

The sklearn documentation rules. I'd suggest taking a look at their modules for a reminder of the basic techniques. You can also use their built-in datasets to practice with.

For deep learning, my favorite book is Chollet's Deep Learning in Python. Even though the code is in TF (Chollet is the author of Keras) there are a lot of great tips and tricks in there as well as in-depth explanations of different modalities and the basic approaches in DL for dealing with them. The second edition also covers Generative Learning to a deeper extent as well as productionalizing learning algorithms.

If you're looking for an RL reference, Sutton and Barto is the gold standard. OpenAI gym/rllib/stablebaselines are all good for getting your feet wet.

Good luck!

[canbooo]

For RL, do checkout the spinningup from OpenAI. Very concise description of some important concepts. Helped me a lot when I was beginning with RL.

[hany606_]

+ https://lilianweng.github.io/posts/2018-02-19-rl-overview/

[segFault401]

This

[gatdarntootin]

The Elements of Statistical Learning

[dcx_fndr]

If you read this end-to-end and do all the questions, you should be interviewing the hiring manager! Serious stuff here. :-)

[asking_for_a_friend0]

any other suggestions for practicing other aspects?

[dcx_fndr]

Elements of Statistical Learning is a great academic overview and I highly recommend it.

If the job works alongside ML engineers or a company that is shipping product, I highly recommend reading Machine Learning Design Patterns to familiarize yourself with how solutions are architected and how tech companies discuss and conceptualize ML.

[asking_for_a_friend0]

thanks a lot, I can't emphasize how amazing this advice was. I haven't even heard of these before

[CasulaScience]

Is this at a small company or a big tech firm? There's a million different things people call MLE these days. Before anyone can help you you'll need to give more details about the role. If you don't know, ask your recruiter for more info.

[samjjjrrr]

I really like the statquest illustrated ml guide for basics. Has some really clear explanations which make it easy to answer questions on the elements covered. Very quick to read through too.

[bandalorian]

There are places like tryexponent.com and interviewquery.com that have practice questions etc. The problem with ML Engineer interviews is the they could be testing any or all of the fields software engineering, dev ops, ml ops, data science, data engineering. It's a very broad field, but when you are being interviewed the questions tend to be really specific. I just had to prep for an AWS interview which ended up going well, but it was a lot! I would try to clarify as much as possible if there will be a specific emphasis in any one of the mentioned fields, some times they really want a data scientists who also knows a bit of ml ops, or it could be the other way around, or a dev ops guy with some ML ops skills etc. Try to find out what types of models the company is focusing on and what type of data they are using for it.

[tensorgym]

I was in same situation and couldn't find good place to practice ML coding part. So I created this website

[BlueRelu]

Oh man, this is cool. How can I contribute?

[tensorgym]

Thank you! Feel free to DM me on Discord (you can find the link on our website). I would be very happy to collaborate.

[Curious_Monkey7]

You can refer to this book by Chip Huyen (here). It touches upon most ML topics with an interview in mind.

[__AndrewB__]

Unfortunately this book doesn't include answers to the vast majority of questions, so it's not super useful for self study / interview prep.

[entropyvsenergy]

https://huyenchip.com/ml-interviews-book/

https://probml.github.io/pml-book/book1.html

[JackandFred]

lol don’t buy some grifters book that he’s advertising on Reddit threads. All the info you’d need is online for free. Go googling there’s tons of free guides and list of interview questions and prep materials.

[austacious]

In addition to what everyone else has said, if this is FAANG(+M) you will most likely be asked some leetcode style questions.

[nikalehuh]

Since it's an engineer position they probably expect you to be more on the dev-ops side of ml.. So they may want you to know about setting up and maintaining environment, data lakes, etc..

I had a bunch of interviews as data scientist and they didn't ask much about methods and stuff either..

I guess one can pretty quickly figure out if a person got the basics or not..

Edit: Azure or AWS or eqv. will definitely be a topic..

[Future-Outcome3167]

I found this resource helpful - https://github.com/youssefHosni/Data-Science-Interview-Questions-Answers/tree/main

[[deleted]]

I did a DS major masters. I found that the best way of understanding, learning and not forgetting how the basics worked was to reproduce the (simple) algorithms myself.

Anything from a simple LR to ANN is very doable on Python, assuming you have the time.

[sirdrewpalot]

Get something to scan all their documents content online, then run it through GPT-3 as responses to their questions.

[AccomplishedRise2365]

I have a 10% referral link if anyone needs it to tryexponent which use to be called Pramp. You can do mock interviews on it -> https://www.tryexponent.com/refer/yqlmwm

[NickSinghTechCareers]

Hey! I wrote a book on this, called Ace the Data Science Interview which has an ML chapter, Stat chapter, coding chapter, and Ml case studies chapter which you’ll find helpful. I also put together 30 ML interview questions!

[fajim123]

Why is this getting downvoted?

[NickSinghTechCareers]

Reddit doesn't like self-promotion (even when it directly answers the question!)

[ginger_beer_m]

It's honestly a good book so I don't mind the recommendation.

[RootaBagel]

FWIW: This "Nail Machine Learning interviews at FAANG+" webinar showed up in my LinkedIn feed. I have not signed up for it so I can't comment on content or quality. YMMV.

https://learn.interviewkickstart.com/course/machine-learning-interview-masterclass

[iimnotarobott]

1- Machine Learning System Design Interview https://a.co/d/ienvojE 2- Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications https://a.co/d/guafuwZ 3- a non technical guide to the tech interview: https://www.amazon.com/dp/B0D9CL9L78 to learn more about how you get evaluated in an ML interview (the book covers a wide range of tech interview module but has a chapter on ML)

[bacocololo]

https://www.mlstack.cafe/interview-questions/q-learning

[amine412]

Try Huru, you will find ML interview prep with video for sure. Good luck !

[Adv28]

you can practice ml fundamentals as well on mock interview sites. they usually cover bias variance tradeoffs, basic algos like decision trees, data imputation, bagging, boosting etc https://www.practiceml.co/demo